Dr. Dallas H Snider

Software defined networks and network function virtualization are providing much needed agility to network and system administrators attempting to meet everchanging demands from their stakeholders. However, there exists a need for tools to assist in the configuration, deployment, testing and knowledge transfer of these networks and their components. In this paper, we present our plans to develop a set of open source tools to assist with the aforementioned tasks. The goal is to apply existing disparate pieces of technology so they may work together to assist with the management of virtual network environments.

With the increase in smart, LEED-certified buildings there comes an increase in the amount of time-series data generated by the sensor networks within these buildings. Extracting useful information from the sensor network data can pose a challenge. While diurnal and seasonal patterns of electrical demand are well known from traditional metering systems, smart building sensor networks can provide insight into abnormalities or previously unknown patterns in electrical demand. In this paper, we demonstrate how to mine the data for these unknowns through the analysis of the frequency components of the time-series electrical demand data. The data for this study was collected from an LEED-certified building over 12 consecutive months with separate feeds for the electrical demand from the heating, A/C, ventilation, lighting, and miscellaneous systems. We employed Fourier methods to transform the data from the time domain to the frequency domain and then used similarity measures to look for similarities and outliers among the differing systems.